Overview Of The AWS Databases: What Service Works Best For You?

Most likely you are already aware of what a database is and of its main functions — processing and storing data. AWS provides a variety of data analytics resources that enable you to easily plan, scale, protect and deploy big data tools. The capabilities for capturing, saving, sorting and analyzing big data significantly differ. 

Users constantly get confused at the point when they need to choose the database that works best for them and their project. So before jumping right into making your decision, you should understand what AWS databases offer.

In this article, we explain the difference between the two AWS database types: SQL and NoSQL, as well as discuss the capabilities, functions and features of the most popular services to help you choose the database that is a perfect match for your project. 

Classification of the AWS Database Services

There are two main AWS database types that we traditionally distinguish in the IT environment: relational (SQL) and NoSQL database management systems. Both of these AWS database options are equally useful in their own way and are entirely possible to work with, but you can still find a ton of differences between them. 

Basiсally, we can classify AWS databases in the following way:

1. Relational Databases

   • Amazon Aurora
   • Amazon RDS
   • Amazon Redshift

2. NoSQL Databases

   • Document DB
   • Dynamo DB
   • Neptune
   • QLDB

Let’s take a closer look at each of them.

Relational Databases

Relational or SQL is the most common database AWS option. The distinctive feature of an SQL database is storing data in interconnected tables. All information in the database is always associated with other information and is presented in a strictly and logically structured way, including the fields that describe the data, the operations performed on them, their relationships with each other and the most important part — the rules that ensure their integrity. 

The table must contain columns (with a data type) and rows (with records). Each row in a table is a record with a key (a unique identifier). Table columns hold data attributes, and each record usually contains a value for each attribute, which helps to easily establish relationships between data items. 

Generally speaking, relational databases are databases that are used to store and provide access to related parts of information.

Here is a useful thing to remember: For ordinary projects, in technical terms, there is no difference which database to use, but economically it would be more beneficial to give preference to the most common MySQL, which is used by many content management systems or slightly less common in simple PostgreSQL projects. With a relational database, you have access to more developers, need less support and have lower development costs. 

On Amazon, there are a variety of uses for relational databases, which can be: 

• CRM (customer relationship management) applications 
• Data on financial transactions 
• Apps for enterprise resource planning (ERP) 
• Data warehousing (is a term that refers to the process of storing)

Benefits of relational databases

• They use SQL (Structured Query Language), which is quite widespread and supports join operations by default.
• The simple structure allows you to effectively work with most data types.
• You have the ability to quickly update data. The entire database is stored on one computer, and the relationships between the records are used as pointers, that is, you can update one record and all records associated with it will be immediately updated.
• Relational databases support atomic transactions.
• Relational databases offer high security. You can limit or allow data access for specific users. 

Disadvantages of relational databases

• Scaling is done by adding computing power to the computer on which the database is installed. This method is called vertical scaling. However, why is this a disadvantage? Because there is a limit to the computing power of a computer and adding resources encourages downtime.
• OOP objects are not supported, so even representing simple lists can be a problem.
• Since each query is against the entire table, the execution time of the query depends on the size of the table. This is an important limitation that forces us to keep tables relatively small and optimize the database to scale.
• The relational data approach is not suitable for all domains.

Let’s consider the most popular Amazon relational databases.

Amazon Aurora

Amazon Aurora has two big advantages: It’s easy to use and offers device efficiency. Aurora’s revolutionary storage infrastructure, which is specifically designed to take advantage of new cloud technology, is one of its most crucial features. Amazon Aurora is five times faster compared to MySQL and three times than PostgreSQL. 

The Amazon Aurora RDBMS storage framework also can be configured based on database workloads. It’s a well-known, high-performance and highly scalable cloud RDBMS that works with MySQL and PostgreSQL relational databases. Since Aurora is compatible with MySQL and PostgreSQL, it can use existing code, programs, drivers and software with little to no changes. Aurora is fully managed so you can set up the database quickly. 

For storage, Amazon Aurora automatically grows in increments of 10 GB, up to 64 TB. When you run serverless, you’re charged in Aurora capacity units (ACUs), which equal 2GB of memory and the compute and network resources that go with it.

Some of the other Aurora features include: 

• Automatic fail-over
• Industry compliance
• Backup and recovery
• Advanced monitoring
• Routine maintenance
• Ability to restore data at any point without backups
• Isolation and security

Amazon Aurora use cases

• Software as a Service (SaaS) offerings — it usually employs multi-talent architectures that should be adaptable in terms of instance and storage scaling. Amazon Aurora enables businesses to concentrate on developing high-quality software rather than thinking about the underlying database.
• Enterprise applications — Amazon Aurora allows utilizing enterprise-level capabilities and functionality, thus lowering prices and reaching millions of AWS customers on the AWS Marketplace or via pre-built templates.
• Web and mobile gaming purpose — the database has massive storage, high out-turn, and high convenience.

Amazon RDS

Amazon Relational Database Service (RDS) permits clients to set up, operate and scale an information base in the AWS database cloud. Amazon RDS gives cost-proficient and resizable limits while robotizing tedious organization assignments (for example, equipment provisioning, arrangement, fixing and reinforcements). 

It liberates you to zero in on applications so you can give them the exhibition and security they need. It is perhaps the most basic and lightweight solution accessible in the market with astounding versatility according to the utilization. 

Amazon RDS enables users to quickly and easily launch database instances and connect applications. RDS is easier to scale because it is less technical, requiring just a few clicks in the AWS Console to calculate an auto-scale total power. It can be used on-demand or with reserved space. 

The engine used affects RDS pricing, but it is normally less expensive than the others. RDS can be purchased as a pay-as-you-go service with a higher tariff, or as a reserved case service with a lower tariff and a commitment to a certain amount of use. Amazon RDS costs less than Aurora, but at the same time, it’s less efficient.

Amazon RDS use cases

• eCommerce applications — Amazon RDS provides small and medium-sized e-commerce businesses a flexible, reliable, highly scalable and cost-effective database solution for online wholesale and retail stores.
• Web and mobile applications — It allows you to set up databases with scalable storage, high throughput and great availability for large-scale web and mobile applications.
• Mobile and online games — Amazon RDS takes control of your database infrastructure so you don’t have to worry about monitoring, provisioning and scaling from developers. Amazon RDS supports popular database engines that can scale quickly in response to user demand.

Amazon Redshift

Amazon Redshift is a fast, completely controlled petabyte storage solution that makes analyzing all of your data with your current business intelligence tools simple and cost-effective. Using advanced database optimization, columnar storage on high-performance local disks and massively parallel query execution, the service helps you to execute complex analytic queries against petabytes of structured data.

Most results can be obtained in a matter of seconds. Additional functions, such as concurrency scaling, are compensated under a separate structure. Redshift also is usable on an allocated instance and an on-demand basis.

Amazon Redshift use cases

• Business analytics —  Redshift makes it easier and less expensive to run high-performance queries on petabytes of structured and semi-structured data. With QuickSight and other business intelligence tools, you can create powerful reports and dashboards.
• Predictive analytics  —  Redshift uses SQL to automatically build, train and deploy Amazon SageMaker models using Redshift Machine Learning Preview to manipulate the data.
• Data as a service —  The sharing feature in Redshift allows you to transfer data both internally and externally in order to process operational data securely and accurately.
• Operational analytics at events — Redshift combines structured warehouse data with semi-structured data from the S3 data lake. This will offer real-time insight into the processes of software and networks.

NoSQL Databases

Unlike most traditional database systems, non-relational databases do not use a tabular row and column schema. They use a storage model that is optimized for the specific requirements of the type of data stored. 

These also are so-called NoSQL databases and storages and include  MongoDB, CouchDB, Redis, Memcached, Cassandra and Scylla. These are much younger than relational databases and also differ significantly from them in storage structure and mechanics of working with data.

NoSQL DBMSs often are used not for storing all application data, but only for solving specific tasks (logging, caching, distributed data storage) and therefore are less common in simple projects. 

While relational databases are not suitable for many use cases, particularly those requiring very high performance or dynamic scalability, NoSQL is there to mainly handle large volumes of unstructured data.  

NoSQL does not store any structured and clear tables, but any information that can be presented in the form of a text document, audio file or publication on the Internet.

Since almost any data can be stored in such databases, they are widely used in a variety of applications for smartphones and PCs. They are ideal for all cases where the structure of understandable data is more important than a flexible and easily scalable database, which also is characterized by high-performance parameters. 

Advantages of NoSQL database

• They allow you to store objects of various structures.
• They can display almost all data structures, including OOP objects, lists and dictionaries, using good old JSON.
• NoSQL queries are super fast. Each record is independent and therefore the query time is independent of the size of the database.
• Although the nature of NoSQL isn’t schematic, they often support schema validation. This means that you can make a collection with a schema. This schema will not be as simple as a table. It will be a JSON schema with specific fields.
• You can make the most out of the AWS Cloud database, delivering zero downtime.
• In NoSQL, database scaling is carried out by adding computers and distributing data between them. It allows you to automatically add resources to the database when you need them, without causing downtime.

Disadvantages of NoSQL database

• Updating data is a slow process in a document database because data can be shared between computers and can be duplicated.
• Atomic transactions are not supported by default. 
• There is strong attachment of the application to a specific DBMS due to the specifics of the internal query language and a flexible data model focused on a specific case.

So we have just covered the main concept. Now let’s review some of the most common AWS database options from the NoSQL group.

DocumentDB

Amazon DynamoDB is a text and key-value database that is fully maintained. It has multi-master and multi-region capabilities, as well as built-in encryption, automatic backup and restores, and in-memory caching. Serverless web applications, microservices and mobile backends will all benefit from DynamoDB.

DynamoDB

Amazon DynamoDB is a database of key-value pairs and documents that delivers less than 10-millisecond latency at scale. It is a robust, fully managed database for web-wide applications that operates in multiple regions with multiple active servers and has built-in security, backup and recovery, and in-memory caching. DynamoDB possesses the ability to work with over 10 trillion requests per day and can overcome peaks in excess of 20 million requests per second.

Neptune

Amazon Neptune is a graph storage service that is entirely run by Amazon. It allows you to build and run applications based on large, interconnected data sets. It allows for the storing of large collections of relationship data with low latency access. RDF, SPARQL and Gremlin are among the graph models and languages supported by Neptune. Point-in-time restore, read replicas and continuous backup also is included.

QLDB

Amazon (QLDB) is a serverless ledger storage provider that is completely run. It can be used to keep track of program data updates in a verifiable manner. You may avoid having to create custom ledger implementations and authentication tools by using QLDB. A SQL-like API can be used to query data in QLDB.

The comparison table below shows the main NoSQL databases services offered by AWS in a more practical way:

Conclusion

As we can see, it is preferable to use a relational DBMS as the main storage. Nonetheless, for ordinary projects, it is easier to use MySQL or PostgreSQL, since the difference between different relational databases is not very noticeable on simple operations. However, if the project provides for a complex logic of data processing, then the choice of the AWS database should be made based on the technical characteristics.  

Traditional SQL databases do an excellent job of handling small, strongly typed information. For example, a local ERP system or a cloud CRM. However, in the case of processing a large amount of semi-structured and unstructured data, i.e. Big Data,   in a distributed system, you should choose from a variety of NoSQL storages, taking into account the specifics of the task itself.

When choosing among the right AWS database types for a project, it’s always important to study different AWS database options and opinions on them from a couple of trusted sources. In the process of making this important decision, it may turn out that the right choice is not one database, but perhaps even a few of them. Choose the best database for solving a specific problem, and that works for your project best!